Systems and methods for updating a webpage

ABSTRACT

In many embodiments, the method can comprise storing one or more user actions of a user of one or more users in a database, sorting the one or more user actions into one or more user action types, and extracting one or more correlated signals related to the one or more user actions of the user of the one or more users based at least in part on the one or more user action types to determine one or more independent signals related to the one or more user actions of the user of the one or more users. In some embodiments, the method can further comprise averaging the one or more independent signals related to the one or more user actions of the user of the one or more users to determine a personalization score related to the user of the one or more users and updating a webpage for the user of the one or more users based at least in part on the personalization score. Other embodiments of related methods and systems are also provided.

TECHNICAL FIELD

This disclosure relates generally to systems for updating a webpage, andrelated methods.

BACKGROUND

System bandwidth can become slow or bottlenecked when retrieving searchresults for a search query. Many times, when a user of a website, suchas an eCommerce website, has difficulty finding an item, the user canconduct numerous user actions and/or item activities (e.g., clicking onone or more items or entering new search terms). These user actionsand/or item activities can decrease the efficiency of a system byincreasing the amount of item information retrieved from a database. Theability to efficiently update a webpage can decrease the demand onsystem resources and improve user experience. Accordingly, there is aneed for systems and methods to provide for updating a webpage.

BRIEF DESCRIPTION OF THE DRAWINGS

To facilitate further description of the embodiments, the followingdrawings are provided in which:

FIG. 1 illustrates a front elevation view of a computer system that issuitable for implementing at least part of a central computer system;

FIG. 2 illustrates a representative block diagram of exemplary elementsincluded on the circuit boards inside a chassis of the computer systemof FIG. 1;

FIG. 3 illustrates a representative block diagram of a system, accordingto an embodiment;

FIG. 4 illustrates a representative block diagram of a portion of thesystem of FIG. 3, according to an embodiment;

FIG. 5 illustrates is a flowchart for a method, according to anembodiment;

FIG. 6 illustrates a flowchart for another method, according to anotherembodiment;

FIG. 7 illustrates a flowchart for yet another method, according to yetanother embodiment;

FIG. 8 illustrates a flowchart for another method, according to stillyet another embodiment; and

FIG. 9 illustrates a flowchart for another method, according to anotherembodiment.

For simplicity and clarity of illustration, the drawing figuresillustrate the general manner of construction, and descriptions anddetails of well-known features and techniques may be omitted to avoidunnecessarily obscuring the present disclosure. Additionally, elementsin the drawing figures are not necessarily drawn to scale. For example,the dimensions of some of the elements in the figures may be exaggeratedrelative to other elements to help improve understanding of embodimentsof the present disclosure. The same reference numerals in differentfigures denote the same elements.

The terms “first,” “second,” “third,” “fourth,” and the like in thedescription and in the claims, if any, are used for distinguishingbetween similar elements and not necessarily for describing a particularsequential or chronological order. It is to be understood that the termsso used are interchangeable under appropriate circumstances such thatthe embodiments described herein are, for example, capable of operationin sequences other than those illustrated or otherwise described herein.Furthermore, the terms “include,” and “have,” and any variationsthereof, are intended to cover a non-exclusive inclusion, such that aprocess, method, system, article, device, or apparatus that comprises alist of elements is not necessarily limited to those elements, but mayinclude other elements not expressly listed or inherent to such process,method, system, article, device, or apparatus.

The terms “left,” “right,” “front,” “back,” “top,” “bottom,” “over,”“under,” and the like in the description and in the claims, if any, areused for descriptive purposes and not necessarily for describingpermanent relative positions. It is to be understood that the terms soused are interchangeable under appropriate circumstances such that theembodiments of the apparatus, methods, and/or articles of manufacturedescribed herein are, for example, capable of operation in otherorientations than those illustrated or otherwise described herein.

The terms “couple,” “coupled,” “couples,” “coupling,” and the likeshould be broadly understood and refer to connecting two or moreelements mechanically and/or otherwise. Two or more electrical elementsmay be electrically coupled together, but not be mechanically orotherwise coupled together. Coupling may be for any length of time,e.g., permanent or semi-permanent or only for an instant. “Electricalcoupling” and the like should be broadly understood and includeelectrical coupling of all types. The absence of the word “removably,”“removable,” and the like near the word “coupled,” and the like does notmean that the coupling, etc. in question is or is not removable.

As defined herein, “approximately” can, in some embodiments, mean withinplus or minus ten percent of the stated value. In other embodiments,“approximately” can mean within plus or minus five percent of the statedvalue. In further embodiments, “approximately” can mean within plus orminus three percent of the stated value. In yet other embodiments,“approximately” can mean within plus or minus one percent of the statedvalue.

DESCRIPTION OF EXAMPLES OF EMBODIMENTS

Some embodiments can include a system. In many embodiments, the systemcan comprise one or more processing modules and one or morenon-transitory storage modules storing computing instructions configuredto run on the one or more processing modules and perform acts. In manyembodiments, the acts can comprise receiving a search query from asearch by a user during a browse session, receiving one or more itemsfrom an item database in response to the search query, and receiving oneor more previous search queries from a search database, the one or moreprevious search queries related to the search query. In manyembodiments, the acts can further comprise determining a purchaseprobability associated with a first item of the one or more items basedat least in part on a first item score for the first item, ranking theone or more items based at least in part on the purchase probabilityassociated with the first item of the one or more items, andfacilitating display of the ranking of the one or more items.

In some embodiments, a method can comprise receiving a search query froma search by a user during a browse session, receiving one or more itemsfrom an item database in response to the search query, and receiving oneor more previous search queries from a search database, the one or moreprevious search queries related to the search query. In manyembodiments, the method can further comprise determining a purchaseprobability associated with a first item of the one or more items basedat least in part on a first item score for the first item, ranking theone or more items based at least in part on the purchase probabilityassociated with the first item of the one or more items, andfacilitating display of the ranking of the one or more items.

Various embodiments can include a method. In many embodiments, themethod can comprise processing data associated with a user of aplurality of users from a browse session by: (1) determining one or morefirst keywords by capturing the one or more first keywords associatedwith a search query of the user of the plurality of users during a timewindow; (2) determining one or more items by capturing the one or moreitems associated with the search query of the user of the plurality ofusers; (3) creating a feature set of data associated with at least aportion of the plurality of users; (4) creating a text corpuscomprising: a search query of the user of the plurality of users and anitem activity associated with the browse session; (5) clustering asearch set based at least in part on the one or more first keywords; and(6) clustering an item set comprising the one or more items. In manyembodiments, the method can further comprise determining an item vectorrepresentation representing the item set, determining a keyword vectorrepresentation representing the search set, and determining a first setof items of the item set as being associated with the search query basedat least in part on the item vector representation and the keywordvector representation. In various embodiments, the method can furthercomprise determining a purchase probability associated with a first itemof the first set of items of the item set based at least in part on afirst item score for the first item, ranking the first set of itemsbased at least in part on the purchase probability associated with thefirst item of the first set of items of the item set, and facilitatingdisplay of the ranking of the first set of items.

Various embodiments comprise a system. In many embodiments, the systemcan comprise one or more processing modules and one or morenon-transitory storage modules storing computing instructions configuredto run on the one or more processing modules and perform acts. In manyembodiments, the acts can comprise receiving one or more clicks on oneor more items by a user during a browse session, measuring a distancebetween the one or more items, and determining a relationship betweenthe one or more items based at least in part on the distance. In someembodiments, the acts can further comprise clustering the one or moreitems based at least in part on the relationship into one or moreclusters and presenting to the user a recommendation. In manyembodiments, the recommendation can comprise at least one of one or moresearch terms related to at least one cluster of the one or more clustersor a set of items related to the at least one cluster of the one or moreclusters, the set of items comprising at least a portion of the one ormore items.

Many embodiments can comprise a method. In some embodiments, the methodcan comprise receiving one or more clicks on one or more items by a userduring a browse session, measuring a distance between the one or moreitems, and determining a relationship between the one or more itemsbased at least in part on the distance. In various embodiments, themethod can further comprise clustering the one or more items based atleast in part on the relationship into one or more clusters andpresenting to the user a recommendation. In a number of embodiments, therecommendation can comprise at least one of one or more search termsrelated to at least one cluster of the one or more clusters or a set ofitems related to the at least one cluster of the one or more clusters,the set of items comprising at least a portion of the one or more items.

Some embodiments can comprise a method. In many embodiments, the methodcan comprise receiving one or more clicks on one or more items by a userduring a browse session and updating a user profile of the user based atleast in part on the one or more clicks on the one or more items by theuser. In many embodiments, the method can further comprise clusteringthe one or more items by measuring a distance between the one or moreitems and determining a relationship between the one or more items basedat least in part on the distance. In some embodiments, the method canfurther comprise presenting to a user one or more search terms relatedto a cluster of items, which are based at least in part on therelationship, and updating the user profile of the user again when theuser clicks on at least one of the one or more search terms.

Various embodiments comprise a system. In many embodiments the systemcan comprise one or more processing modules and one or morenon-transitory storage modules storing computing instructions configuredto run on the one or more processing modules and perform acts. In someembodiments, the acts can comprise receiving a search query from asearch by a user and determining a question to present to the user. Inmany embodiments, determining the question to present to the user cancomprise evaluating a user profile associated with the user, evaluatingthe search query, evaluating one or more user actions during a currentbrowse session of the user, and selecting the question from a set ofquestions. In some embodiments, the acts can further comprise presentingthe question to the user when a confidence score associated with thequestion reaches or exceeds a predetermined threshold.

Some embodiments can comprise a method. In many embodiments, the methodcan comprise receiving a search query from a search by a user anddetermining a question to present to the user. In many embodiments,determining the question to present to the user can comprise evaluatinga user profile associated with the user, evaluating the search query,evaluating one or more user actions during a current browse session ofthe user, and selecting the question from a set of questions. In someembodiments, the method can further comprise presenting the question tothe user when a confidence score associated with the question reaches orexceeds a predetermined threshold.

Many embodiments can comprise a method. In some embodiments, the methodcan comprise determining a set of questions associated with a campaignby extracting text from one or more advertisements. In some embodiments,the method can further comprise determining a question from the set ofquestions to present to a user by evaluating a user profile associatedwith the user, evaluating a search query from the user, evaluating oneor more user actions during a current browse session of the user, andselecting the question from the set of questions. In a number ofembodiments, the method can further comprise presenting the question tothe user when a confidence score associated with the question reaches orexceeds a predetermined threshold.

Various embodiments can comprise a system. In some embodiments, thesystem can comprise one or more processing modules and one or morenon-transitory storage modules storing computing instructions configuredto run on the one or more processing modules and perform acts. In manyembodiments, the acts can comprise storing one or more user actions of auser of one or more users in a database, sorting the one or more useractions into one or more user action types, and extracting one or morecorrelated signals related to the one or more user actions of the userof the one or more users based at least in part on the one or more useraction types to determine one or more independent signals related to theone or more user actions of the user of the one or more users. In someembodiments, the acts can further comprise averaging the one or moreindependent signals related to the one or more user actions of the userof the one or more users to determine a personalization score related tothe user of the one or more users and updating a webpage for the user ofthe one or more users based at least in part on the personalizationscore.

Some embodiments can comprise a method. In many embodiments, the methodcan comprise storing one or more user actions of a user of one or moreusers in a database, sorting the one or more user actions into one ormore user action types, and extracting one or more correlated signalsrelated to the one or more user actions of the user of the one or moreusers based at least in part on the one or more user action types todetermine one or more independent signals related to the one or moreuser actions of the user of the one or more users. In some embodiments,the method can further comprise averaging the one or more independentsignals related to the one or more user actions of the user of the oneor more users to determine a personalization score related to the userof the one or more users and updating a webpage for the user of the oneor more users based at least in part on the personalization score.

A number of embodiments can comprise a method. In some embodiments, themethod can comprise updating a user profile of a user of one or moreusers based at least in part on a determination of a personalizationscore of the user of the one or more users. In many embodiments,determining the personalization score of the user of the one or moreusers can comprise storing one or more user actions of the user of theone or more users in a database, sorting the one or more user actionsinto one or more user action types, extracting one or more correlatedsignals related to the one or more user actions of the user of the oneor more users based at least in part on the one or more user actiontypes to determine one or more independent signals related to the one ormore user actions of the user of the one or more users, and averagingthe one or more independent signals related to the one or more useractions of the user to determine the personalization score related tothe user of the one or more users.

Turning to the drawings, FIG. 1 illustrates an exemplary embodiment of acomputer system 100, all of which or a portion of which can be suitablefor (i) implementing part or all of one or more embodiments of thetechniques, methods, and systems and/or (ii) implementing and/oroperating part or all of one or more embodiments of the memory storagemodules described herein. As an example, a different or separate one ofa chassis 102 (and its internal components) can be suitable forimplementing part or all of one or more embodiments of the techniques,methods, and/or systems described herein. Furthermore, one or moreelements of computer system 100 (e.g., a monitor 106, a keyboard 104,and/or a mouse 110, etc.) also can be appropriate for implementing partor all of one or more embodiments of the techniques, methods, and/orsystems described herein. Computer system 100 can comprise chassis 102containing one or more circuit boards (not shown), a Universal SerialBus (USB) port 112, a Compact Disc Read-Only Memory (CD-ROM) and/orDigital Video Disc (DVD) drive 116, and a hard drive 114. Arepresentative block diagram of the elements included on the circuitboards inside chassis 102 is shown in FIG. 2. A central processing unit(CPU) 210 in FIG. 2 is coupled to a system bus 214 in FIG. 2. In variousembodiments, the architecture of CPU 210 can be compliant with any of avariety of commercially distributed architecture families.

Continuing with FIG. 2, system bus 214 also is coupled to a memorystorage unit 208, where memory storage unit 208 can comprise (i)non-volatile (e.g., non-transitory) memory, such as, for example, readonly memory (ROM) and/or (ii) volatile (e.g., transitory) memory, suchas, for example, random access memory (RAM). The non-volatile memory canbe removable and/or non-removable non-volatile memory. Meanwhile, RAMcan include dynamic RAM (DRAM), static RAM (SRAM), etc. Further, ROM caninclude mask-programmed ROM, programmable ROM (PROM), one-timeprogrammable ROM (OTP), erasable programmable read-only memory (EPROM),electrically erasable programmable ROM (EEPROM) (e.g., electricallyalterable ROM (EAROM) and/or flash memory), etc. The memory storagemodule(s) of the various embodiments disclosed herein can comprisememory storage unit 208, an external memory storage drive (not shown),such as, for example, a USB-equipped electronic memory storage drivecoupled to universal serial bus (USB) port 112 (FIGS. 1-2), hard drive114 (FIGS. 1-2), a CD-ROM and/or DVD for use with a CD-ROM and/or DVDdrive 116 (FIGS. 1-2), floppy disk for use with a floppy disk drive (notshown), an optical disc (not shown), a magneto-optical disc (now shown),magnetic tape (not shown), etc. Further, non-volatile or non-transitorymemory storage module(s) refer to the portions of the memory storagemodule(s) that are non-volatile (e.g., non-transitory) memory.

In various examples, portions of the memory storage module(s) of thevarious embodiments disclosed herein (e.g., portions of the non-volatilememory storage module(s)) can be encoded with a boot code sequencesuitable for restoring computer system 100 (FIG. 1) to a functionalstate after a system reset. In addition, portions of the memory storagemodule(s) of the various embodiments disclosed herein (e.g., portions ofthe non-volatile memory storage module(s)) can comprise microcode suchas a Basic Input-Output System (BIOS) operable with computer system 100(FIG. 1). In the same or different examples, portions of the memorystorage module(s) of the various embodiments disclosed herein (e.g.,portions of the non-volatile memory storage module(s)) can comprise anoperating system, which can be a software program that manages thehardware and software resources of a computer and/or a computer network.The BIOS can initialize and test components of computer system 100(FIG. 1) and load the operating system. Meanwhile, the operating systemcan perform basic tasks such as, for example, controlling and allocatingmemory, prioritizing the processing of instructions, controlling inputand output devices, facilitating networking, and managing files.Exemplary operating systems can comprise one of the following: (i)Microsoft® Windows® operating system (OS) by Microsoft Corp. of Redmond,Wash. United States of America, (ii) Mac® OS X by Apple Inc. ofCupertino, Calif., United States of America, (iii) UNIX® OS, and (iv)Linux® OS. Further exemplary operating systems can comprise one of thefollowing: (i) the iOS® operating system by Apple Inc. of Cupertino,Calif., United States of America, (ii) the Blackberry® operating systemby Research In Motion (RIM) of Waterloo, Ontario, Canada, (iii) theWebOS operating system by LG Electronics of Seoul, South Korea, (iv) theAndroid™ operating system developed by Google, of Mountain View, Calif.,United States of America, (v) the Windows Mobile™ operating system byMicrosoft Corp. of Redmond, Wash., United States of America, or (vi) theSymbian™ operating system by Accenture PLC of Dublin, Ireland.

As used herein, “processor” and/or “processing module” means any type ofcomputational circuit, such as but not limited to a microprocessor, amicrocontroller, a controller, a complex instruction set computing(CISC) microprocessor, a reduced instruction set computing (RISC)microprocessor, a very long instruction word (VLIW) microprocessor, agraphics processor, a digital signal processor, or any other type ofprocessor or processing circuit capable of performing the desiredfunctions. In some examples, the one or more processing modules of thevarious embodiments disclosed herein can comprise CPU 210.

In the depicted embodiment of FIG. 2, various I/O devices such as a diskcontroller 204, a graphics adapter 224, a video controller 202, akeyboard adapter 226, a mouse adapter 206, a network adapter 220, andother I/O devices 222 can be coupled to system bus 214. Keyboard adapter226 and mouse adapter 206 are coupled to keyboard 104 (FIGS. 1-2) andmouse 110 (FIGS. 1-2), respectively, of computer system 100 (FIG. 1).While graphics adapter 224 and video controller 202 are indicated asdistinct units in FIG. 2, video controller 202 can be integrated intographics adapter 224, or vice versa in other embodiments. Videocontroller 202 is suitable for monitor 106 (FIGS. 1-2) to display imageson a screen 108 (FIG. 1) of computer system 100 (FIG. 1). Diskcontroller 204 can control hard drive 114 (FIGS. 1-2), USB port 112(FIGS. 1-2), and CD-ROM drive 116 (FIGS. 1-2). In other embodiments,distinct units can be used to control each of these devices separately.

Network adapter 220 can be suitable to connect computer system 100(FIG. 1) to a computer network by wired communication (e.g., a wirednetwork adapter) and/or wireless communication (e.g., a wireless networkadapter). In some embodiments, network adapter 220 can be plugged orcoupled to an expansion port (not shown) in computer system 100 (FIG.1). In other embodiments, network adapter 220 can be built into computersystem 100 (FIG. 1). For example, network adapter 220 can be built intocomputer system 100 (FIG. 1) by being integrated into the motherboardchipset (not shown), or implemented via one or more dedicatedcommunication chips (not shown), connected through a PCI (peripheralcomponent interconnector) or a PCI express bus of computer system 100(FIG. 1) or USB port 112 (FIG. 1).

Returning now to FIG. 1, although many other components of computersystem 100 are not shown, such components and their interconnection arewell known to those of ordinary skill in the art. Accordingly, furtherdetails concerning the construction and composition of computer system100 and the circuit boards inside chassis 102 are not discussed herein.

Meanwhile, when computer system 100 is running, program instructions(e.g., computer instructions) stored on one or more of the memorystorage module(s) of the various embodiments disclosed herein can beexecuted by CPU 210 (FIG. 2). At least a portion of the programinstructions, stored on these devices, can be suitable for carrying outat least part of the techniques and methods described herein.

Further, although computer system 100 is illustrated as a desktopcomputer in FIG. 1, there can be examples where computer system 100 maytake a different form factor while still having functional elementssimilar to those described for computer system 100. In some embodiments,computer system 100 may comprise a single computer, a single server, ora cluster or collection of computers or servers, or a cloud of computersor servers. Typically, a cluster or collection of servers can be usedwhen the demand on computer system 100 exceeds the reasonable capabilityof a single server or computer. In certain embodiments, computer system100 may comprise a portable computer, such as a laptop computer. Incertain other embodiments, computer system 100 may comprise a mobileelectronic device, such as a smartphone. In certain additionalembodiments, computer system 100 may comprise an embedded system.

Skipping ahead now in the drawings, FIG. 3 illustrates a representativeblock diagram of a system 300, according to an embodiment. System 300 ismerely exemplary and embodiments of the system are not limited to theembodiments presented herein. System 300 can be employed in manydifferent embodiments or examples not specifically depicted or describedherein. In some embodiments, certain elements or modules of system 300can perform various methods and/or activities of those methods. In theseor other embodiments, the methods and/or the activities of the methodscan be performed by other suitable elements or modules of system 300.

Generally, therefore, system 300 can be implemented with hardware and/orsoftware, as described herein. In some embodiments, part or all of thehardware and/or software can be conventional, while in these or otherembodiments, part or all of the hardware and/or software can becustomized (e.g., optimized) for implementing part or all of thefunctionality of system 300 described herein.

In a number of embodiments, system 300 can comprise a search system 310,a personalization system 320, and a display system 360. In someembodiments, search system 310, personalization system 320, and displaysystem 360 can each be a computer system 100 (FIG. 1), as describedabove, and can each be a single computer, a single server, or a clusteror collection of computers or servers. In some embodiments, searchsystem 310 and/or personalization system 320 can be in communicationwith an inventory database (not shown) which can track distinct items(e.g., stock keeping units (SKUs)), and images of the distinct items, ina product catalog, which can be ordered through the online retailer andwhich can be housed at one or more warehouses. In many embodiments,warehouses can comprise brick-and-mortar stores, distribution centers,and/or other storage facilities.

In many embodiments, search system 310, personalization system 320,and/or display system 360 can each comprise one or more input devices(e.g., one or more keyboards, one or more keypads, one or more pointingdevices such as a computer mouse or computer mice, one or moretouchscreen displays, a microphone, etc.), and/or can each comprise oneor more display devices (e.g., one or more monitors, one or more touchscreen displays, projectors, etc.). In these or other embodiments, oneor more of the input device(s) can be similar or identical to keyboard104 (FIG. 1) and/or a mouse 110 (FIG. 1). Further, one or more of thedisplay device(s) can be similar or identical to monitor 106 (FIG. 1)and/or screen 108 (FIG. 1). The input device(s) and the displaydevice(s) can be coupled to the processing module(s) and/or the memorystorage module(s) of search system 310, personalization system 320,and/or display system 360 in a wired manner and/or a wireless manner,and the coupling can be direct and/or indirect, as well as locallyand/or remotely. As an example of an indirect manner (which may or maynot also be a remote manner), a keyboard-video-mouse (KVM) switch can beused to couple the input device(s) and the display device(s) to theprocessing module(s) and/or the memory storage module(s). In someembodiments, the KVM switch also can be part of search system 310,personalization system 320, and/or display system 360. In a similarmanner, the processing module(s) and the memory storage module(s) can belocal and/or remote to each other.

In many embodiments, search system 310 and/or display system 360 can beconfigured to communicate with one or more user computers 340 and 341.In some embodiments, user computers 340 and 341 also can be referred toas customer computers. In some embodiments, search system 310 and/ordisplay system 360 can communicate or interface (e.g. interact) with oneor more customer computers (such as user computers 340 and 341) througha network 330. In some embodiments, network 330 can be an internet, anintranet that is not open to the public, an email system, and/or atexting system. In many embodiments, network 330 can comprise one ormore electronic transmission channels. In many embodiments, theelectronic transmission channels can comprise an email, a text message,and/or an electronic notice or message. Accordingly, in manyembodiments, search system 310 and/or display system 360 (and/or thesoftware used by such systems) can refer to a back end of system 300operated by an operator and/or administrator of system 300, and usercomputers 340 and 341 (and/or the software used by such systems) canrefer to a front end of system 300 used by one or more users 350 and351, respectively. In some embodiments, users 350 and 351 also can bereferred to as customers, in which case, user computers 340 and 341 canbe referred to as customer computers. In these or other embodiments, theoperator and/or administrator of system 300 can manage system 300, theprocessing module(s) of system 300, and/or the memory storage module(s)of system 300 using the input device(s) and/or display device(s) ofsystem 300.

Meanwhile, in many embodiments, search system 310, personalizationsystem 320, and/or display system 360 also can be configured tocommunicate with one or more databases. The one or more database cancomprise a product database that contains information about products,items, or SKUs sold by a retailer. The one or more databases can bestored on one or more memory storage modules (e.g., non-transitorymemory storage module(s)), which can be similar or identical to the oneor more memory storage module(s) (e.g., non-transitory memory storagemodule(s)) described above with respect to computer system 100 (FIG. 1).Also, in some embodiments, for any particular database of the one ormore databases, that particular database can be stored on a singlememory storage module of the memory storage module(s), and/or thenon-transitory memory storage module(s) storing the one or moredatabases or the contents of that particular database can be spreadacross multiple ones of the memory storage module(s) and/ornon-transitory memory storage module(s) storing the one or moredatabases, depending on the size of the particular database and/or thestorage capacity of the memory storage module(s) and/or non-transitorymemory storage module(s).

The one or more databases can each comprise a structured (e.g., indexed)collection of data and can be managed by any suitable databasemanagement systems configured to define, create, query, organize,update, and manage database(s). Exemplary database management systemscan include MySQL (Structured Query Language) Database, PostgreSQLDatabase, Microsoft SQL Server Database, Oracle Database, SAP (Systems,Applications, & Products) Database, and IBM DB2 Database.

Meanwhile, communication between search system 310, personalizationsystem 320, display system 360, and/or the one or more databases can beimplemented using any suitable manner of wired and/or wirelesscommunication. Accordingly, system 300 can comprise any software and/orhardware components configured to implement the wired and/or wirelesscommunication. Further, the wired and/or wireless communication can beimplemented using any one or any combination of wired and/or wirelesscommunication network topologies (e.g., ring, line, tree, bus, mesh,star, daisy chain, hybrid, etc.) and/or protocols (e.g., personal areanetwork (PAN) protocol(s), local area network (LAN) protocol(s), widearea network (WAN) protocol(s), cellular network protocol(s), powerlinenetwork protocol(s), etc.). Exemplary PAN protocol(s) can compriseBluetooth, Zigbee, Wireless Universal Serial Bus (USB), Z-Wave, etc.;exemplary LAN and/or WAN protocol(s) can comprise Institute ofElectrical and Electronic Engineers (IEEE) 802.3 (also known asEthernet), IEEE 802.11 (also known as WiFi), etc.; and exemplarywireless cellular network protocol(s) can comprise Global System forMobile Communications (GSM), General Packet Radio Service (GPRS), CodeDivision Multiple Access (CDMA), Evolution-Data Optimized (EV-DO),Enhanced Data Rates for GSM Evolution (EDGE), Universal MobileTelecommunications System (UMTS), Digital Enhanced CordlessTelecommunications (DECT), Digital AMPS (IS-136/Time Division MultipleAccess (TDMA)), Integrated Digital Enhanced Network (iDEN), EvolvedHigh-Speed Packet Access (HSPA+), Long-Term Evolution (LTE), WiMAX, etc.The specific communication software and/or hardware implemented candepend on the network topologies and/or protocols implemented, and viceversa. In many embodiments, exemplary communication hardware cancomprise wired communication hardware including, for example, one ormore data buses, such as, for example, universal serial bus(es), one ormore networking cables, such as, for example, coaxial cable(s), opticalfiber cable(s), and/or twisted pair cable(s), any other suitable datacable, etc. Further exemplary communication hardware can comprisewireless communication hardware including, for example, one or moreradio transceivers, one or more infrared transceivers, etc. Additionalexemplary communication hardware can comprise one or more networkingcomponents (e.g., modulator-demodulator components, gateway components,etc.)

Turning ahead in the drawings, FIG. 5 illustrates a flow chart for amethod 500, according to an embodiment. Method 500 is merely exemplaryand is not limited to the embodiments presented herein. Method 500 canbe employed in many different embodiments or examples not specificallydepicted or described herein. In some embodiments, the activities ofmethod 500 can be performed in the order presented. In otherembodiments, the activities of method 500 can be performed in anysuitable order. In still other embodiments, one or more of theactivities of method 500 can be combined or skipped. In manyembodiments, system 300 (FIG. 3) can be suitable to perform method 500and/or one or more of the activities of method 500. In these or otherembodiments, one or more of the activities of method 500 can beimplemented as one or more computer instructions configured to run atone or more processing modules and configured to be stored at one ormore non-transitory memory storage modules 412, 414, 422, 424, and/or462 (FIG. 4). Such non-transitory memory storage modules can be part ofa computer system such as search system 310 (FIGS. 3 & 4),personalization system 320 (FIGS. 3 & 4), and/or display system 360(FIGS. 3 & 4). The processing module(s) can be similar or identical tothe processing module(s) described above with respect to computer system100 (FIG. 1). In many embodiments, method 500 can be similar to method600 (FIG. 6), method 700 (FIG. 7), and/or method 800 (FIG. 8). In manyembodiments, portions of method 600 (FIG. 6), method 700 (FIG. 7),method 800 (FIG. 8) and/or method 900 (FIG. 9) can be used within method500.

In many embodiments, method 500 can be a method to personalize a webpage based on user intent, information from search query (e.g., searchterms) and/or other item activity. For example, method 500 can comprisean activity 505 of receiving a search query from a search by a userduring a browse session. In some embodiments, the browse session cancomprise a time period spent on a website and/or other third partywebsites. In some embodiments, the time period can be approximately 1second to approximately 1 hour. In some embodiments, the time period canbe the time that the user is logged into a session. In some embodiments,the time period can be from when the user logs into a session to whenthe user closes a browser. In some embodiments, receiving the searchquery from the search by the user can comprise receiving the searchquery during a time window. In some embodiments, the time window cancomprise the browse session time period. In some embodiments, the timewindow can comprise a number of item activity associated with the browsesession. In various embodiments, the item activity associated with thebrowse session can comprise at least one of a view of an item of theitem set, a click on the item of the item set, an add-to-cart of theitem of the item set, or a purchase of the item of the item set. In anumber of embodiments, the time window can comprise a number of actions,subsequent to the search query, associated with item activity associatedwith the browse session (e.g., a number of clicks on one or more items,a number of views of one or more items, a number of items added to thecheckout cart, and/or a number of purchases of one or more items). Insome embodiments the number of subsequent actions can comprise acombination of a number of item activities. In some embodiments, thenumber of subsequent actions can comprise approximately 1 to 100 itemactivities.

In many embodiments, method 500 can further comprise an activity 510 ofreceiving one or more items from an item database in response to thesearch query. In some embodiments, method 500 can further comprise anactivity 515 of receiving one or more previous search queries from asearch database, the one or more previous search queries related to thesearch query. An advantage of activity 515 of receiving one or moreprevious search queries from a search database, the one or more previoussearch queries related to the search query, can comprise expanding asource of information associated with previous search queries for one ormore searches related to the search query. The source of information cancomprise when an other user searched for a related search query and theitem activity associated to the other user's search for the relatedsearch query.

In various embodiments, method 500 can further comprise an activity ofprocessing data associated with at least a portion of a plurality ofusers. In many embodiments, the plurality of users can comprise the userand the data can comprise historical online behavior associated with theat least the portion of the plurality of users. In some embodiments, thehistorical online behavior can comprise at least one of: a user searchquery, a view of an item of the item set, a click on the item of theitem set, an add-to-cart of the item of the item set, or a purchase ofthe item of the item set. In some embodiments, the historical onlinebehavior can comprise a length of time a user of the at least theportion of the plurality of users viewed an item of the item set. Insome embodiments, the data associated with the at least the portion ofthe plurality of users can comprise one or more user profiles associatedwith the at least the portion of the plurality of users. In variousembodiments, the one or more user profiles can comprise demographicinformation associated with the related one or more users, likes anddislikes associated with the related one or more users, and/or shopping,pickup, and delivery preferences associated with the related one or moreusers.

In a number of embodiments, method 500 can further comprise processingdata associated with the user of the plurality of users from the browsesession by determining one or more first keywords by capturing the oneor more first keywords associated with the search query of the user ofthe plurality of users (e.g., the search query received in activity 505)during the time window.

In a number of embodiments, method 500 can further comprise processingdata associated with the user of the plurality of users from the browsesession by creating a feature set of data associated with at least aportion of the plurality of users and/or creating a text corpuscomprising the search query of the user of the plurality of users (e.g.,the search query received in activity 505) and an item activityassociated with the browse session. In some embodiments, method 500 canfurther comprise processing data associated with the user of theplurality of users from the browse session by clustering a search setbased at least in part on the one or more first keywords and clusteringan item set comprising the one or more items.

In a number of embodiments, method 500 can further comprise processingdata associated with the user of the plurality of users from the browsesession by determining the one or more items by capturing the one ormore items (e.g., the one or more items received in activity 510)associated with the search query of the user of the plurality of users.

In various embodiments, method 500 can further comprise an activity ofdetermining an item vector representation representing the item setand/or determining a keyword vector representation representing thesearch set. In some embodiments, method 500 can comprise an activity ofdetermining a first set of items of the item set as being associatedwith the search query based at least in part on the item vectorrepresentation and the keyword vector representation.

In some embodiments, natural language modeling can be used to learnvector representation of the search query (e.g., item vectorrepresentation and/or the keyword vector representation). In someembodiments, the natural language modeling can comprise one-hotrepresentation of the keywords (e.g., the keywords in the vocabulary ofa size V, wherein each input keyword vector is of a size V_(i) and anoutput keyword vector is of a size V₀). In some embodiments, an input tothe natural language modeling can be a word from the search query, andthe output can be a context of the word. As a non-limiting example, if abrowse session comprises the following search queries and/or itemactivity, for a search query “q” in a browse session at time “t,” a timewindow size “S” can be selected. Any item activity from time “t+1” canbe considered as context for the browse session.

{    “1”: “homepage”,    “2”: “category Page”,    ″3″: ″View item1″,   ″4″: ″search frozen toys″,    ″5″: ″category Page″,    ″6″: ″searchfrozen dolls″,    ″7″: ″View item2″,    ″8″: ″add item2 to cart″,   ″9″: ″view item3″,    ″10″:  ″add item3 to cart″,    ″11″:  ″checkoutwith item2, item3″ }

For a time window of size=5 and a search query of “Frozen toys,” onlythe 5 subsequent actions by the user after the search query of “Frozentoys” can be considered as the context of the browse session. In manyembodiments, preceding item activity is not considered as context forthe browse session.

In many embodiments, method 500 can further comprise training a neuralnet model to provide context of the browse session. In some embodiments,Stochastic Gradient Descent can be used to train the neural net model.In various embodiments, a vocabulary size “V” can comprise the one ormore first keywords associated with the search query and the one or moreitems captured by the search query. In some embodiments, the neural netmodel can comprise a hidden layer size “N,” a window size “S,” and themodel can comprise the following matrix structure:[1×V]→[V×N]→[1×N]→[S×N]→[N×V]→[S×V].

In some embodiments, the input is a one-hot encoded vector, which canmean that for a given input word, only one out of V units, {x_(i), . . ., x,}, will be 1, and all other units are 0. The [VxN] matrix can be theweight matrix W between an input layer and the hidden layer whose j^(th)row can represent one or more weights corresponding to the j^(th) wordin the vocabulary. Hence, this weight matrix can provide the vectorrepresentations of all words in the vocabulary.

In many embodiments, method 500 can further comprise determining one ormore nearest items and/or one or more nearest previous search queries(e.g., the one or more previous search queries received in activity 515)by identifying clusters and/or clustering the search set based at leastin part on the one or more first keywords, and clustering an item setcomprising the one or more items. In many embodiments, the search querycan be expanded based at least in part on the identifying clustersand/or clustering the search set based at least in part on the one ormore first keywords and clustering an item set comprising the one ormore items. In various embodiments, natural language processing andunderstanding can be used to expand the search query and augmentprecision of cluster similarity.

In various embodiments, method 500 can further comprise an activity 520of determining a purchase probability associated with a first item ofthe one or more items based at least in part on a first item score forthe first item. In some embodiments, the first item score can be basedat least in part on item activity associated with the first item fromprevious search queries. In some embodiments, activity 520 can furthercomprise determining a purchase probability associated with a seconditem of the one or more items based at least in part on a second itemscore for the second item. In some embodiments, activity 520 can furthercomprise determining a purchase probability associated with a third itemof the one or more items based at least in part on a third item scorefor the third item. In some embodiments, activity 520 can furthercomprise determining a purchase probability associated with a fourthitem of the one or more items based at least in part on a fourth itemscore for the fourth item. In some embodiments, the purchase probabilitycan comprise a probability that a user will purchase a particular item(e.g., the first, second, third, and/or fourth item of the one or moreitems) on a given day (e.g., the present day of the browse session). Insome embodiments, activity 520 of determining a purchase probabilityassociated with a first item of the one or more items based at least inpart on a first item score for the first item can comprise training amultinomial logistic regression model.

In a number of embodiments, method 500 can further comprise an activity525 of ranking the one or more items based at least in part on thepurchase probability associated with the first item of the one or moreitems.

In many embodiments, method 500 can further comprise an activity 530 offacilitating display of the one or more items on the webpage. The one ormore items can be arranged on the webpage pursuant to a ranking of oneor more items, and the ranking can be based, at least in part, on thewebpage personalization described herein.

The other variations described below for method 600 (FIG. 6), method 700(FIG. 7), method 800 (FIG. 8) and/or method 900 (FIG. 9) also can applyhere to method 500.

FIG. 6 illustrates a flow chart for a method 600, according to anembodiment. Method 600 is merely exemplary and is not limited to theembodiments presented herein. Method 600 can be employed in manydifferent embodiments or examples not specifically depicted or describedherein. In some embodiments, the activities of method 600 can beperformed in the order presented. In other embodiments, the activitiesof method 600 can be performed in any suitable order. In still otherembodiments, one or more of the activities of method 600 can be combinedor skipped. In many embodiments, system 300 (FIG. 3) can be suitable toperform method 600 and/or one or more of the activities of method 600.In these or other embodiments, one or more of the activities of method600 can be implemented as one or more computer instructions configuredto run at one or more processing modules and configured to be stored atone or more non-transitory memory storage modules 412, 414, 422, 424and/or 462 (FIG. 4). Such non-transitory memory storage modules can bepart of a computer system such as search system 310 (FIGS. 3 & 4),personalization system 320 (FIGS. 3 & 4), and/or display system 360(FIGS. 3 & 4). The processing module(s) can be similar or identical tothe processing module(s) described above with respect to computer system100 (FIG. 1). In many embodiments, method 600 can be similar to method500 (FIG. 5), method 700 (FIG. 7), and/or method 800 (FIG. 8). In manyembodiments, portions of method 500 (FIG. 5), method 700 (FIG. 7),method 800 (FIG. 8) and/or method 900 (FIG. 9) can be used within method600.

In many embodiments, method 600 can be a method for personalizing webpages based on user intent and relevance from search terms. For example,method 600 can comprise an activity 605 of receiving one or more clickson one or more items by a user during a browse session. In manyembodiments, activity 605 can comprise receiving item activityassociated with the browse session. In some embodiments, item activityassociated with the browse session that does not comprise a search querycan be labeled as “noSearch.”

In many embodiments, method 600 can further comprise an activity 610 ofmeasuring a distance between the one or more items. In many embodiments,activity 610 of measuring the distance between the one or more times cancomprise measuring a similarity between the one or more items. In someembodiments, activity 610 of measuring the distance between the one ormore times can comprise measuring a Jaccard index between the one ormore items. In some embodiments, activity 610 can be based at least inpart on determining an item vector representation representing the itemset and/or determining a keyword vector representation representing thesearch set, similar to method 500 described above.

In some embodiments, method 600 can further comprise an activity 615 ofdetermining a relationship between the one or more items based at leastin part on the distance. In some embodiments, activity 615 can be basedat least in part on determining an item vector representationrepresenting the item set and/or determining a keyword vectorrepresentation representing the search set, similar to method 500described above. In many embodiments, the relationship between the oneor more items can comprise a coefficient of similarity or a Jaccardcoefficient. In some embodiments, activity 615 can further comprisedetermining that a number of the one or more clicks meets or exceeds apredetermined threshold before presenting to the user therecommendation. In some embodiments, the predetermined threshold can beapproximately 1-30 item clicks. An advantage of determining arelationship between the one or more items based at least in part on thedistance is that a higher number of clicks on items with similarity or aclose distance can indicate that the user knows what he is searchingfor, but cannot determine the correct search term (e.g., a user canenter a search query of “shoes” and can click a number of times on shoeswith closed lacing, in which case the method can determine that the useris searching for “Oxford shoes” and can present the term as arecommendation for a subsequent search). Another advantage ofdetermining the relationship between the one or more items based atleast in part on the distance is that a high number of clicks ondissimilar items can indicate that the user is browsing and, in someembodiments, a recommendation is not presented.

In some embodiments, activity 615 can further comprise creating a textcorpus similar to the text corpus described above in method 500, thetext corpus comprising the search query of the user of the plurality ofusers and/or an item activity associated with the browse session (e.g.,the text clicks received in activity 605). In various embodiments,activity 615 can further comprise an activity of determining an itemvector representation representing the item set and/or determining akeyword vector representation representing the search set, similar tomethod 500 described above. In many embodiments, a natural languagemodel can be used to determine the item vector representationrepresenting the item set and/or the keyword vector representationrepresenting the search set. In some embodiments, the natural languagemodel can use high dimensional embedding for feature representationwithin the item vector representation representing the item set and/orthe keyword vector representation representing the search set. In someembodiments, the high dimensional representation can be tuned to a modelcausality (e.g., an abstract model that describes causal mechanism of asystem).

In various embodiments, method 600 can further comprise an activity 620of clustering the one or more items based at least in part on therelationship into one or more clusters. In many embodiments, activity620 of clustering the one or more items based at least in part on therelationship into one or more clusters can be similar to clustering asearch set and/or clustering an item set as described above in method500.

In many embodiments, method 600 can further comprise training arecurrent neural network model (e.g., a long short-term memory recurrentneural network architecture) to predict a next action (e.g., itemactivity or search query) the user can perform, given one or moreactions (e.g., item activity, search query, past history, and/or pastactions) during the browse session. In many embodiments, method 600 canpredict a probability of a user performing a given action in view of theuser's previous actions. In some embodiments, the browse session can bedivided into one or more chunks “N,” with each chunk “N” having aminimum length of time. In some embodiments, the minimum length of timecan comprise 1 time unit (e.g., the time unit can be correlated to anumber of seconds or minutes the user spent in the browse session). Insome embodiments, the probability of a user performing the given actioncan be based at least in part on a density estimate. In someembodiments, the density can be estimated at point “x” according to thefollowing formula:p(x)=(k*a)/(v*n),wherein, “v” is a volume of hypercube surrounding “x”, “n” is a totalnumber of points, “k” is a number of query points inside “v”, “a” is anumber of items out of “m” number of items the user has interacted with(e.g., item activity) during the browse session that are present inside“v.” In many embodiments, a total density p{x} can be calculated for all“m” items. In some embodiments, a highest density within p{x} can beselected and the candidate queries can be returned (e.g., recommended inactivity 625, described below). Similarly, in some embodiments, aprobability score of the user performing one or more actions (e.g., itemactivity) can be determined for searches at a time “t,” given the one ormore actions (e.g., item activity) the user has performed at time “t−1.”

In some embodiments, method 600 can further comprise an activity 625 ofpresenting to the user a recommendation. In many embodiments, therecommendation can comprise one or more search terms related to at leastone cluster of the one or more clusters and/or or a set of items relatedto the at least one cluster of the one or more clusters, the set ofitems comprising at least a portion of the one or more items. In anumber of embodiments, the recommendation is presented only after theuser has returned to a homepage. One reason for limiting thepresentation in this way is that when the user returns to the homepage,it can signal that the user is done with the previous search, and/or wasnot satisfied with the previous search. In which case, a recommendationcan assist the user in efficiently completing a new search. In a numberof embodiments, activity 625 can further comprise an activity ofreceiving a search query from the user during the browse session. Inmany embodiments the browse session can be similar to the browse sessionof method 500. In some embodiments, activity 625 can further comprise anactivity of updating the one or more clusters based at least in part onthe search query. In many embodiments, activity 625 can further compriseevaluating a past history of one or more past actions of the user and/orother users in a previous browse session and updating the one or moreclusters based at least in part on the past history of the one or morepast actions of the user and/or other users. In some embodiments, theone or more past actions can comprise one or more other search queriesby the user and/or other users, one or more item clicks by the userand/or other users, one or more items added-to-cart by the user and/orother users, and/or one or more item purchases by the user and/or otherusers. In many embodiments, based on the time period of the browsesession and the one or more items the user has interacted (e.g., itemactivity) with during the time session, an intent of the user can bedetermined. In some embodiments, if the user interacts with one or moreitems such that it is determined that the user has a click rate (e.g., arate at which the user clicks on one or more items) or bounce rate(e.g., a rate at which the user leaves a webpage or one or more items)above the predetermined threshold, the intent of the user can bedetermined to comprise browsing and searching more, and therefore therecommendation can comprise one or more new search term (e.g., searchquery and/or search topic).

In some embodiments, the probability score for one or more potentialqueries or recommendations (e.g., recommended in activity 625, describedbelow) can be used to re-rank the one or more potential queries orrecommendations (e.g., recommended in activity 625, described below). Insome embodiments, a recommended query with a highest probability scorecan be ranked first, and therefore recommended first. In someembodiments, only recommendations with a probability score that reachesor exceeds a predetermined threshold can be presented to the user. Insome embodiments, the predetermined threshold can comprise a probabilityscore of approximately 0.5 or 50 percent.

In many embodiments, method 600 can further comprise evaluating a userprofile of the user and updating the one or more clusters based at leastin part on the user profile of the user. In some embodiments, the userprofile can comprise a past history of the user. In many embodiments,the past history can comprise a browse history, a search history, apurchase history, an item add-to-cart history, and/or an item clickhistory. In many embodiments, method 600 can further comprise updatingthe user profile when the user clicks on the recommendation. In someembodiments, method 600 can further comprise updating the user profilewhen the user adds a recommended item or an item from a recommendedsearch term to a checkout cart and/or purchases the recommended item orthe item from a recommended search term.

In some embodiments, method 600 can further comprise presenting aquestion to the user based at least in part on the one or more clicks onthe one or more items and updating the one or more clusters based atleast in part on an answer to the question presented similar to method700 (FIG. 7).

The other variations described above for method 500 (FIG. 5) or belowfor method 700 (FIG. 7), method 800 (FIG. 8), and/or method 900 (FIG. 9)also can apply here to method 600.

FIG. 7 illustrates a flow chart for a method 700, according to anembodiment. Method 700 is merely exemplary and is not limited to theembodiments presented herein. Method 700 can be employed in manydifferent embodiments or examples not specifically depicted or describedherein. In some embodiments, the activities of method 700 can beperformed in the order presented. In other embodiments, the activitiesof method 700 can be performed in any suitable order. In still otherembodiments, one or more of the activities of method 700 can be combinedor skipped. In many embodiments, system 300 (FIG. 3) can be suitable toperform method 700 and/or one or more of the activities of method 700.In these or other embodiments, one or more of the activities of method700 can be implemented as one or more computer instructions configuredto run at one or more processing modules and configured to be stored atone or more non-transitory memory storage modules 412, 414, 422, 424and/or 462 (FIG. 4). Such non-transitory memory storage modules can bepart of a computer system such as search system 310 (FIGS. 3 & 4),personalization system 320 (FIGS. 3 & 4), and/or display system 360(FIGS. 3 & 4). The processing module(s) can be similar or identical tothe processing module(s) described above with respect to computer system100 (FIG. 1). In many embodiments, method 700 can be similar to method500 (FIG. 5), method 600 (FIG. 6), and/or method 800 (FIG. 8). In manyembodiments, portions of method 500 (FIG. 5), method 600 (FIG. 6),method 800 (FIG. 8) and/or method 900 (FIG. 9) can be used within method700.

In many embodiments, method 700 can be a method for personalized querysuggestions using browsing patterns. For example, method 700 cancomprise an activity 705 of receiving a search query from a search by auser. In many embodiments, activity 605 can be similar to activity 505(FIG. 5).

In many embodiments, method 700 can further comprise an activity 710 ofdetermining a question to present to the user. In many embodiments,activity 710 can further comprise determining the question to present tothe user by, for example, evaluating a user profile associated with theuser, evaluating the search query, evaluating one or more user actionsduring a current browse session of the user, and/or selecting thequestion from a set of questions. In many embodiments, the currentbrowse session can be referred to as a browse session similar to asdescribed above in method 500. In many embodiments, the one or more useractions can be similar to an item activity as described above. In someembodiments, the one or more user actions can be one or more othersearch queries by the user and/or other users, one or more item clicksby the user and/or other users, one or more items added-to-cart by theuser and/or other users, or one or more item purchases by the userand/or other users.

In some embodiments, method 700 can further comprise an activity 715 ofpresenting the question to the user when a confidence score associatedwith the question reaches or exceeds a predetermined threshold. In someembodiments, the confidence score can be based at least in part on auser profile of the user. In many embodiments, the user profile cancomprise a past history of the user. In some embodiments, method 700 canfurther comprise an activity of evaluating a past history of purchasesby the user and/or other users. In some embodiments, the past historycan comprise a browse history, a search history, a purchase history, anitem add-to-cart history, and/or an item click history.

In some embodiments, the confidence score can be based at least in parton a grouping of the user within a set of users. In some embodiments,one or more users who have created a baby registry can be grouped in a“new mom” grouping. In some embodiments, a confidence score for a userwho has been grouped in a “new mom” grouping (e.g., has created a babyregistry) can be higher than a confidence score for a user who hasrecently purchased an item off of a baby registry (e.g., a guest at ababy shower). This is example, the confidence score for the user who hasbeen grouped in a “new mom” grouping is more likely to have reached orexceeded the predetermined threshold for presenting a questionassociated with newborn babies.

In some embodiments, the predetermined threshold can be lower for askinga first-type question than for asking a second-type question. In someembodiments, questions can be tiered in a taxonomy, with questions inhigher numbered tiers comprising more detailed information. In someembodiments, the first tier comprises category-level questions. In someembodiments, the second tier and/or higher tier comprise questionsrelated to details of the one or more items. In some embodiments, afirst-type question can be a question with a second tier or higherquestion (e.g., a question associated with personal demographicinformation of the user or associated with an attribute of an item suchas “are you searching for a 60′ television?”) while a second-typequestion can be a first tier question (e.g., a question associated witha category of items or interests, such as “are you searching for atelevision?”). In some embodiments, a user with a user profile that isapproximately 50 percent or more complete can be asked second-typequestions, while a user with a user profile that is less thanapproximately 50 percent complete can be asked first-type questions. Anadvantage of using a predetermined threshold to determine a type ofquestion is that it increases efficiency by decreasing the use ofresources by decreasing the number of tiers within the taxonomy tosearch.

In some embodiments, an answer to the question presented can be storedin the user profile of the user. In many embodiments, method 700 canfurther comprise selecting a question based at least in part on the userprofile of the user. An advantage of storing the answer to the questionpresented in the user profile is that it prevents a question from beingpresented more than once to a user, this efficiency can decrease the useof resources and efficiently store information in memory for use later.

In many embodiments, the question can be presented in order to increasethe efficiency of the search and/or to provide an improved searchexperience to the user. Improving the efficiency of the search candecrease the use of resources, including computer network bandwidth, bydecreasing the number of categories or taxonomies within the iteminventory or website to search once a directed question has beenpresented and answered.

In some embodiments, the question can be presented during a transitiontime during the browse session. In some embodiments, the transition timecan comprise a time when the click rate and/or bounce rate reaches orexceeds a predetermined threshold, and/or a time when the user isentering or running a new search query. In some embodiments, thepredetermined threshold for the bounce rate can comprise when the userhas clicked on at least approximately 5-30 items. In some embodiments,the predetermined threshold for the bounce rate can comprise when theuser has viewed an item for less than approximately 2-5 seconds. In manyembodiments, the question is not presented during a time when the useris adding one or more items to the cart and/or during a checkout time.In a number of embodiments, the question can be presented in a dialogbox, a banner, an email, a text message, or a pop-up box.

In some embodiments, method 700 can further comprise determining the setof questions by extracting text from one or more advertisements.

The other variations described above for method 500 (FIG. 5) and method600 (FIG. 6) or below for method 800 (FIG. 8) and/or method 900 (FIG. 9)also can apply here to method 700.

FIG. 8 illustrates a flow chart for a method 800, according to anembodiment. Method 800 is merely exemplary and is not limited to theembodiments presented herein. Method 800 can be employed in manydifferent embodiments or examples not specifically depicted or describedherein. In some embodiments, the activities of method 800 can beperformed in the order presented. In other embodiments, the activitiesof method 800 can be performed in any suitable order. In still otherembodiments, one or more of the activities of method 800 can be combinedor skipped. In many embodiments, system 300 (FIG. 3) can be suitable toperform method 800 and/or one or more of the activities of method 800.In these or other embodiments, one or more of the activities of method800 can be implemented as one or more computer instructions configuredto run at one or more processing modules and configured to be stored atone or more non-transitory memory storage modules 412, 414, 422, 424and/or 462 (FIG. 4). Such non-transitory memory storage modules can bepart of a computer system such as search system 310 (FIGS. 3 & 4),personalization system 320 (FIGS. 3 & 4), and/or display system 360(FIGS. 3 & 4). The processing module(s) can be similar or identical tothe processing module(s) described above with respect to computer system100 (FIG. 1). In many embodiments, method 800 can be similar to method500 (FIG. 5), method 600 (FIG. 6), and/or method 700 (FIG. 7). In manyembodiments, portions of method 500 (FIG. 5), method 600 (FIG. 6),method 700 (FIG. 7), and/or method 900 (FIG. 9) can be used withinmethod 800.

In many embodiments, method 800 can comprise an activity 805 of storingone or more user actions of a user of one or more users in a database.In many embodiments, the one or more user actions can comprise actionssimilar to item activity as described above in method 500 (FIG. 5). Inmany embodiments, the one or more user actions can comprise one or moreother search queries by the user and/or other users, one or more itemclicks by the user and/or other users, one or more items added-to-cartby the user and/or other users, a time spent on a webpage by the userand/or other users, and/or a bounce rate; or one or more item purchasesby the user and/or other users. In many embodiments, the one or moreusers can comprise the other users.

In many embodiments, method 800 can further comprise an activity 810 ofsorting the one or more user actions into one or more user action types.In some embodiments, the one or more user action types can comprise aone or more of search, a search query, a click, an add-to-cart, a viewtime, or a purchase.

In some embodiments, method 800 can further comprise an activity 815 ofextracting one or more correlated signals related to the one or moreuser actions of the user of the one or more users based at least in parton the one or more user action types to determine one or moreindependent signals related to the one or more user actions of the userof the one or more users. In one embodiment, activity 815 can compriseusing a Mahalanobis transformation Σ^(−1/2)α_(i) to transform a vectorα_(i), wherein α_(i) is the vector of the one or more user actions by ani^(th) user of the one or more users to determine the one or moreindependent signals related to the one or more user actions of the userof the one or more users (e.g., to produce independent signals ofuniform variability). In some embodiments, when the actions arecorrelated, the dimension of vector α_(i) can be reduced.

In various embodiments, method 800 can further comprise an activity 820of averaging the one or more independent signals related to the one ormore user actions of the user to determine a personalization scorerelated to the user of the one or more users. In some embodiments,activity 820 can comprise averaging using the formula:

$\mu^{relevant} = {{\frac{1}{n}{\sum\limits_{i = 1}^{n}\;{\Sigma^{{- 1}\text{/}2}a_{i}}}} = {\Sigma^{{- 1}\text{/}2}\mu}}$wherein μ is the mean vector of the one or more user actions, α_(i) isthe vector of the one or more user actions by an i^(th) user of the oneor more users, Σ^(−1/2) is the ZCA-Mahalanobis whitening matrix, n is atotal number of the one or more users and μ^(relevant) is an averagevector of relevant signals. In many embodiments, each component of theaverage vector of relevant signals μ^(relevant) can independentlymeasure at least one aspect of an average quality of a user experience.In some embodiments, activity 820 can comprise determining a weightedaverage of the average vector of one or more independent signals (e.g.,the relevant signals). In some embodiments, determining the weightedaverage can comprise selecting one or more weights which can bedependent on one or more predetermined goals (e.g., increase traffic toan advertisement campaign). In some embodiments, in the absence ofpredetermined goals, each signal of the one or more independent signalscan be weighted equally. An advantage of measuring the average qualityof the user experience comprises increasing search efficiency whileincreasing a likelihood of engaging the user more effectively in orderto meet the user's needs.

In many embodiments, method 800 further can comprise an activity 825 ofapplying a weighted average on the average vector of the one or more oneor more independent signals (e.g., the relevant signals) to obtain apersonalization score. In some embodiments, the weighted average can beapplied on the average vector using the following method

User ID Clicks Revenue 1 0 20 2 1 0 3 2 0 4 0 0Wherein:Click mean=(¼)*(0+1+2+0)=¾;Revenue mean=(¼)*(20+0+0+0)=5;Clickvariance=(¼)*[(0−¾)^({circumflex over ( )}2)+(1−¾)^({circumflex over ( )}2)+(2−¾)^({circumflex over ( )}2)+(0−¾)^({circumflex over ( )}2)]=0.6875;Revenuevariance=¼[(20−5)^({circumflex over ( )}2)+(0−5)^({circumflex over ( )}2)+(0−5)^({circumflex over ( )}2)]+=75;Click-revenuecovariance=¼[(0*20−¾*5)+(1*0−¾*5)+(2*0−¾*5)+(0*0−¾*5)]=−15/4;Mean vector (μ)=[0.75, 5];Co-variance metric (Σ)=[[0.6875, −15/4][−15/4, 75]]; andCustomers n=4;Σ^({circumflex over ( )}(−)½)=[[1.41270502, 0.06530326], [0.06530326,0.11861205]];

The average vector of independent relevant signals of quality of userexperience=μ^(relevant)=Σ^({circumflex over ( )}(−1/2))μ=[1.386045,0.6420377]. When each signal of the one or more independent signals isweighted equally, the personalization scoreis=½*1.386045+½*0.6420377=1.0140. In some embodiments, therecommendation can be personalized to increase another variable, such asrevenue. For example, let R_t be the revenue on t-th day and μ¹ _(t) andμ² _(t) are the first and second independent signals for the t-th day.Then, through regression:R_t=2*μ¹ _(t)+3*μ² _(t)+ε_(t)

The weights can then change accordingly and the personalization scorecan be computed as=2/5*1.386045+3/5*0.6420377=0.9396406

In many embodiments, method 800 can further comprise an activity 830 ofupdating a webpage for the user based at least in part on thepersonalization score. In some embodiments, method 800 can furthercomprise an activity of amending a campaign (e.g., decreasing emails tothe user, increasing emails to the user, changing an advertisement onthe webpage, and/or suggesting one or more search terms or queries) whenthe personalization score is below a predetermined threshold.

In some embodiments, method 800 further can comprise presenting aquestion to the user based at least in part on the one or more actionsof the user of the one or more users and/or when the personalizationscore is below a predetermined threshold, similar to method 700 (FIG. 7)and/or method 600 (FIG. 6), as described above. In some embodiments,method 800 can comprise updating a user profile of a user of one or moreusers based at least in part on a determination of a personalizationscore of the user of the one or more users. In some embodiments, method800 further can comprise updating a user profile of the user thepersonalization score reaches or exceeds a predetermined threshold.

The other variations described above for method 500 (FIG. 5), method 600(FIG. 6) and/or method 700 (FIG. 7), or below for method 900 (FIG. 9)also can apply here to method 800.

FIG. 9 illustrates a flow chart for a method 900, according to anembodiment. Method 900 is merely exemplary and is not limited to theembodiments presented herein. Method 900 can be employed in manydifferent embodiments or examples not specifically depicted or describedherein. In some embodiments, the activities of method 900 can beperformed in the order presented. In other embodiments, the activitiesof method 900 can be performed in any suitable order. In still otherembodiments, one or more of the activities of method 900 can be combinedor skipped. In many embodiments, system 300 (FIG. 3) can be suitable toperform method 900 and/or one or more of the activities of method 900.In these or other embodiments, one or more of the activities of method900 can be implemented as one or more computer instructions configuredto run at one or more processing modules and configured to be stored atone or more non-transitory memory storage modules 412, 414, 422, 424and/or 462 (FIG. 4). Such non-transitory memory storage modules can bepart of a computer system such as search system 310 (FIGS. 3 & 4),personalization system 320 (FIGS. 3 & 4), and/or display system 360(FIGS. 3 & 4). The processing module(s) can be similar or identical tothe processing module(s) described above with respect to computer system100 (FIG. 1). In many embodiments, method 900 can be similar to method500 (FIG. 5), method 600 (FIG. 6), and/or method 800 (FIG. 8). In manyembodiments, portions of method 500 (FIG. 5), method 600 (FIG. 6),and/or method 800 (FIG. 8) can be used within method 700.

In many embodiments, method 900 can be a method for personalized querysuggestions using one or more online activities of the user. In manyembodiments, method 900 can be similar to method 700 (FIG. 7). Forexample, method 900 can comprise an activity 905 of receiving one ormore online activities of a user. In many embodiments, activity 905 canbe similar to activity 705 (FIG. 7). In some embodiments, the one ormore online activities can be similar to item activity as discussedabove. In some embodiments, activity 905 can comprise capturing and/orreceiving one or more user segments associated with the user, the one ormore user segments can comprise demographics of the user, likes and/ordislikes of the user, and/or preferred brands or items of the user.

In many embodiments, method 900 further can comprise an activity 910 ofdetermining one or more questions associated with the one or more onlineactivities of the user. In many embodiments, activity 910 can be similarto activity 710 (FIG. 7). In many embodiments, natural languageprocessing can be used to determine the one or more questions. Forexample, one or more questions can be identified as combination ofsubject, object and/or predicate phrases. The natural languageprocessing model can help build related questions from set of subjects,objects and predicates for a given vocabulary. In some embodiments,method 900 also can comprise an activity 915 of classifying the one ormore questions into one or more tiers. In many embodiments, activity 915can be used as a representative to classify questions based onparameters such as vagueness. For example, question “are you looking fornew shoes?” is vaguer than the question “are you looking for Nikeshoes?”

In some embodiments, method 900 further can comprise an activity 920 ofbuilding a model, wherein the model predicts a probability associatedwith each of the one or more questions. In many embodiments, activity920 can comprise building vector embedding of the one or more onlineactivities. In some embodiments, activity 920 further can predict a userintent of the user in the current browse session based at least in parton high dimensional embedding for search queries and item browse by theuser and/or other users. In various embodiments, activity 920 furthercan comprise training a supervised model such as deep neural network topredict relevant questions from activity 920 for given onlineactivities, user intent and/or user segments as the input vector to theneural network. For example, the model can intelligently identify aquestion like “would you like to browse latest games for your new Xbox?”by knowing that the user recently purchased an Xbox and has shown userintent to browse and/or search for games based at least in part on theone or more online activities of the user.

In some embodiments, method 900 further can comprise an activity 925 ofpresenting at least one of the one or more questions to the user basedat least in part on the probability of each of the one or morequestions. In many embodiments, activity 925 can be similar to activity715 (FIG. 7). In some embodiments, activity 925 can comprise presentingthe at least one of the one or more questions to the customer during thebrowse session or through other communication mediums, such as email.

In a number of embodiments, method 900 further can comprise an activity930 of updating the model based at least in part on feedback from theuser. In many embodiments, activity 930 further can comprise trackingengagement data of the user with the at least one of the one or morequestions in form of views, clicks, and/or activities after clicks, etc.In many embodiments, the engagement data can be used to trainreinforcement learning models and/or feedback loop for the same user andalso for other user whose online activities, user intent, and/or usersegments are comprise similarities to those of the user.

The other variations described above for method 500 (FIG. 5), method 600(FIG. 6), method 700 (FIG. 7), and/or method 800 (FIG. 8) also can applyhere to method 900.

Returning to FIG. 4, FIG. 4 illustrates a block diagram of a portion ofsystem 300 comprising search system 310, personalization system 320,and/or display system 360, according to the embodiment shown in FIG. 3.Each of search system 310, personalization system 320, and/or displaysystem 360 are merely exemplary and are not limited to the embodimentspresented herein. Each of search system 310, personalization system 320,and/or display system 360 can be employed in many different embodimentsor examples not specifically depicted or described herein. In someembodiments, certain elements or modules of search system 310,personalization system 320, and/or display system 360 can performvarious procedures, processes, and/or acts. In other embodiments, theprocedures, processes, and/or acts can be performed by other suitableelements or modules.

In many embodiments, search system 310 can comprise non-transitorymemory storage modules 412 and 414, personalization system 320 cancomprise non-transitory memory storage modules 422 and 424, and displaysystem 360 can comprise a non-transitory memory storage module 462.Memory storage module 412 can be referred to as a browse module 412 andmemory storage module 414 can be referred to as a search module 414.Memory storage module 422 can be referred to as a probability module 422and memory storage module 424 can be referred to as a recommendationmodule. Memory storage module 462 can be referred to as an image module462.

In many embodiments, browse module 412 can store computing instructionsconfigured to run on one or more processing modules and perform one ormore acts of methods 500 (FIG. 5) (e.g., activity 505), one or more actsof method 600 (FIG. 6) (e.g., activity 605), one or more acts of method700 (e.g., activity 705), one or more acts of method 800 (e.g., activity805), and/or one or more acts of method 900 (e.g., activity 905).

In some embodiments, search module 414 can store computing instructionsconfigured to run on one or more processing modules and perform one ormore acts of methods 500 (FIG. 5) (e.g., activity 510 and/or activity515), one or more acts of method 600 (FIG. 6) (e.g., activity 605), oneor more acts of method 700 (e.g., activity 705), one or more acts ofmethod 800 (e.g., activity 805), and/or one or more acts of method 900(e.g., activity 905).

In many embodiments, probability module 422 can store computinginstructions configured to run on one or more processing modules andperform one or more acts of methods 500 (FIG. 5) (e.g., activity 520),one or more acts of method 600 (FIG. 6) (e.g., activity 610, activity615, and/or activity 620), one or more acts of method 700 (e.g.,activity 710), one or more acts of method 800 (e.g., activity 810,activity 815, activity 820 and/or 825), and/or one or more acts ofmethod 900 (e.g., activity 910, activity 915, activity 920, and/oractivity 925).

In many embodiments, recommendation module 424 can store computinginstructions configured to run on one or more processing modules andperform one or more acts of methods 500 (FIG. 5) (e.g., activity 525),one or more acts of method 600 (FIG. 6) (e.g., activity 625), one ormore acts of method 700 (e.g., activity 710), and/or one or more acts ofmethod 800 (e.g., activity 830), and/or one or more acts of method 900(e.g., activity 925).

In some embodiments, image module 462 can store computing instructionsconfigured to run on one or more processing modules and perform one ormore acts of methods 500 (FIG. 5) (e.g., activity 530), one or more actsof method 600 (FIG. 6) (e.g., activity 625), one or more acts of method700 (e.g., activity 715), and/or one or more acts of method 800 (e.g.,activity 825) and/or one or more acts of method 900 (e.g., activity925).

Although systems and methods for search result comparison been describedabove, it will be understood by those skilled in the art that variouschanges may be made without departing from the spirit or scope of thedisclosure. Accordingly, the disclosure of embodiments is intended to beillustrative of the scope of the disclosure and is not intended to belimiting. It is intended that the scope of the disclosure shall belimited only to the extent required by the appended claims. For example,to one of ordinary skill in the art, it will be readily apparent thatany element of FIGS. 1-9 may be modified, and that the foregoingdiscussion of certain of these embodiments does not necessarilyrepresent a complete description of all possible embodiments. Forexample, one or more of the activities of FIGS. 5-9 may includedifferent activities and/or be performed by many different modules, inmany different orders.

Replacement of one or more claimed elements constitutes reconstructionand not repair. Additionally, benefits, other advantages, and solutionsto problems have been described with regard to specific embodiments. Thebenefits, advantages, solutions to problems, and any element or elementsthat may cause any benefit, advantage, or solution to occur or becomemore pronounced, however, are not to be construed as critical, required,or essential features or elements of any or all of the claims, unlesssuch benefits, advantages, solutions, or elements are stated in suchclaim.

Moreover, embodiments and limitations disclosed herein are not dedicatedto the public under the doctrine of dedication if the embodiments and/orlimitations: (1) are not expressly claimed in the claims; and (2) are orare potentially equivalents of express elements and/or limitations inthe claims under the doctrine of equivalents.

What is claimed is:
 1. A system comprising: one or more processors; andone or more non-transitory computer-readable media storing computinginstructions configured to run on the one or more processors andperform: storing two or more user actions of a user of one or more usersin a database; sorting the two or more user actions into two or moreuser action types; extracting, by a Mahalanbois transformation, two ormore correlated signals related to the two or more user actions of theuser of the one or more users based at least in part on the two or moreuser action types to determine two or more independent signals relatedto the two or more user actions of the user of the one or more users;averaging the two or more independent signals related to the two or moreuser actions of the user of the one or more users to determine apersonalization score related to the user of the one or more users; andafter the user of the one or more users has returned to a homepage of awebsite, recommending one or more search terms for a search query basedat least in part on the personalization score.
 2. The system of claim 1,wherein: the two or more user actions comprise at least two differentones of: one or more other search queries by the user of the one or moreusers; one or more item clicks by the user of the one or more users; oneor more items added-to-cart by the user of the one or more users; a timespent on a webpage of the website by the user of the one or more users;a bounce rate; or one or more item purchases by the user of the one ormore users.
 3. The system of claim 1, wherein: the two or more useraction types comprise at least two different ones of: a search; a click;an add-to-cart; a view time; or a purchase.
 4. The system of claim 1,wherein: averaging the two or more independent signals related to thetwo or more user actions of the user of the one or more users comprisesdetermining a weighted average of the two or more independent signalsrelated to the two or more user actions of the user of the one or moreusers.
 5. The system of claim 1, wherein: averaging the two or moreindependent signals related to the two or more user actions of the userof the one or more users comprises averaging using a formula:$\mu^{relevant} = {{\frac{1}{n}{\sum\limits_{i = 1}^{n}\;{\Sigma^{{- 1}\text{/}2}a_{i}}}} = {\Sigma^{{- 1}\text{/}2}\mu}}$wherein: μ is a mean vector of the two or more user actions; a₁ is avector of the two or more user actions by an i^(th) user of the one ormore users; Σ^(−1/2) is a ZCA-Mahalanobis whitening matrix; n is a totalnumber of the one or more users; and μ^(relevant) is an average vectorof independent signals.
 6. The system of claim 1, wherein: the computinginstructions are further configured to perform the acts of: amending acampaign when the personalization score is below a predeterminedthreshold.
 7. The system of claim 1, wherein: the computing instructionsare further configured to perform: updating a user profile of the userof the one or more users when the personalization score reaches orexceeds a predetermined threshold.
 8. A method comprising: storing twoor more user actions of a user of one or more users in a database;sorting the two or more user actions into two or more user action types;extracting, by a Mahalanbois transformation, two or more correlatedsignals related to the two or more user actions of the user of the oneor more users based at least in part on the two or more user actiontypes to determine two or more independent signals related to the two ormore user actions of the user of the one or more users; averaging thetwo or more independent signals related to the two or more user actionsof the user to determine a personalization score related to the user ofthe one or more users; and after the user of the one or more users hasreturned to a homepage of a website, recommending one or more searchterms for a search query based at least in part on the personalizationscore.
 9. The method of claim 8, wherein: the two or more user actionscomprise at least two different ones of: one or more other searchqueries by the user of the one or more users; one or more item clicks bythe user of the one or more users; one or more items added-to-cart bythe user of the one or more users; a time spent on a webpage of thewebsite by the user of the one or more users; a bounce rate; or one ormore item purchases by the user of the one or more users.
 10. The methodof claim 8, wherein: the two or more user action types comprise at leasttwo different ones of: a search; a click; an add-to-cart; a view time;or a purchase.
 11. The method of claim 8, wherein: averaging the two ormore independent signals related to the two or more user actions of theuser of the one or more users comprises determining a weighted averageof the two or more independent signals related to the two or more useractions of the user of the one or more users.
 12. The method of claim 8,wherein: averaging the two or more independent signals related to thetwo or more user actions of the user of the one or more users comprisesaveraging using a formula:$\mu^{relevant} = {{\frac{1}{n}{\sum\limits_{i = 1}^{n}\;{\Sigma^{{- 1}\text{/}2}a_{i}}}} = {\Sigma^{{- 1}\text{/}2}\mu}}$wherein: μ is a mean vector of the two or more user actions; a₁ is avector of the two or more user actions by an i^(th) user of the one ormore users; Σ^(−1/2) is a ZCA-Mahalanobis whitening matrix; n is a totalnumber of the one or more users; and μ^(relevant) is an average vectorof independent signals.
 13. The method of claim 8, further comprising:amending a campaign when the personalization score is below apredetermined threshold.
 14. The method of claim 8, further comprising:updating a user profile of the user of the one or more users when thepersonalization score reaches or exceeds a predetermined threshold. 15.A method comprising: updating a user profile of a user of one or moreusers based at least in part on a determination of a personalizationscore of the user of the one or more users, wherein determining thepersonalization score of the user of the one or more users comprises:storing two or more user actions of the user of the one or more users ina database; sorting the two or more user actions into two or more useraction types; extracting, by a Mahalanbois transformation, two or morecorrelated signals related to the two or more user actions of the userof the one or more users based at least in part on the two or more useraction types to determine two or more independent signals related to thetwo or more user actions of the user of the one or more users; averagingthe two or more independent signals related to the two or more useractions of the user to determine the personalization score related tothe user of the one or more users; and after the user of the one or moreusers has returned to a homepage of a website, recommending one or moresearch terms for a search query based at least in part on thepersonalization score.
 16. The method of claim 15, wherein: the two ormore user actions comprise at least two different ones of: one or moreother search queries by the user of the one or more users; one or moreitem clicks by the user of the one or more users; one or more itemsadded-to-cart by the user of the one or more users; a time spent on awebpage of the website by the user of the one or more users; a bouncerate; or one or more item purchases by the user of the one or moreusers.
 17. The method of claim 15, wherein: the two or more user actiontypes comprise at least two different ones of: a search; a click; anadd-to-cart; a view time; or a purchase.
 18. The method of claim 15,wherein: averaging the two or more independent signals related to thetwo or more user actions of the user of the one or more users comprisesdetermining a weighted average of the two or more independent signalsrelated to the two or more user actions of the user of the one or moreusers.
 19. The method of claim 8, wherein: averaging the two or moreindependent signals related to the two or more user actions of the userof the one or more users comprises averaging using a formula:$\mu^{relevant} = {{\frac{1}{n}{\sum\limits_{i = 1}^{n}\;{\Sigma^{{- 1}\text{/}2}a_{i}}}} = {\Sigma^{{- 1}\text{/}2}\mu}}$wherein: μ is a mean vector of the two or more user actions; a₁ is avector of the two or more user actions by an i^(th) user of the one ormore users; Σ^(−1/2) is a ZCA-Mahalanobis whitening matrix; n is a totalnumber of the one or more users; and μ^(relevant) is an average vectorof independent signals.
 20. The method of claim 8, further comprising:amending a campaign when the personalization score is below apredetermined threshold.